As conventional von-Neumann architectures are suffering from rising power densities, we are facing an era with power, energy efficiency, and cooling as first-class constraints for scalable HPC. FPGAs can tailor the hardware to the application, avoiding overheads and achieving higher hardware efficiency than general-purpose architectures. Leading FPGA manufacturers have recently made a concerted effort to provide a range of higher-level, easier to use high-level programming models for FPGAs.

Such initiatives are already stimulating new interest within the HPC community around the potential advantages of FPGAs over other architectures. With this in mind, this workshop, now in its third year, brings together HPC and heterogeneous-computing researchers to demonstrate and share experiences on legacy and new high-level programming models, optimizations specific to scientific computing and data analytics, tools for performance/energy improvements, FPGA computing in the cloud, and popular applications for reconfigurable computing such as machine learning and big data.

H2RC is a half-day Friday workshop. It will be comprised of a keynote, invited talks and talks selected from paper submissions.

SUBMISSION GUIDELINES:
Submissions are solicited that explore the state of the art in the use of FPGAs in heterogeneous high-performance computing architectures and, at a system level, in data centers and supercomputers. FPGAs may be considered from either or both the distributed, parallel and composable fabric of compute elements or from their dynamic reconfigurability. We particularly encourage submissions which focus on the mapping of algorithms and applications to heterogeneous FPGA-based systems as well as the overall impact of such architectures on the compute capacity, cost, power efficiency, and overall computational capabilities of data centers and supercomputers. Submissions may report on theoretical or applied research, implementation case studies, benchmarks, standards, or any other area that promises to make a significant contribution to our understanding of heterogeneous high-performance reconfigurable computing and will help to shape future research and implementations in this domain.

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